Methods and systems to detect eating

ABSTRACT

Embodiments include methods and systems for automated eating detection. Systems comprise a continuous glucose monitor (CGM), accelerometer, and processing unit. During a first time period, the processing unit receives glucose readings and a first set of acceleration readings. The processing unit identifies an eating episode if the glucose readings satisfy one or more criteria. Using the identified eating episode and first set of acceleration readings, the processing unit generates an individual model that identifies eating episodes using acceleration readings without using glucose readings. During a second time period, a processing unit uses the individual model and a second set of acceleration readings to identify a second eating episode. Some embodiments may use additional sensor types (for example, PPG or heart rate) to identify eating episodes in the first or second time period, generate the individual model, or any combination thereof.

REFERENCE TO TEXT FILE APPENDIX

A computer program listing has been submitted herewith as an ASCII textfile and is incorporated by reference in the specification pursuant toMPEP 608.05 I (“Text Files Submitted by EFS-Web”). The name of the ASCIItext file is “Appendix_S191V.0020USP_TXT,” created on Apr. 23, 2018 witha size of 23 kilobytes.

TECHNICAL FIELD

Present embodiments relate to the use of continuous glucose monitors(CGMs) to detect eating episodes for subsequent use in detecting eatingepisodes using accelerometer data.

BACKGROUND

Automatically generated data on food consumption has many potentialapplications. Individuals may be interested in monitoring their owneating behavior for reasons including weight management and control ofblood glucose levels. Logging food intake is an established exercise tomake individuals more aware of what they eat. However, keeping a writtenlog at the time of food consumption is cumbersome and may often beskipped. Simpler methods, such as taking pictures of foods eaten, may beeasily forgotten. Recording food consumption at the end of a day relieson memory, which may be prone to error, particularly with respect to thetime at which an individual ate particular foods. In addition, if a foodlog is used in a coaching program, the coach may not learn about foodconsumption until long after it has occurred. A system that quicklysends a notification when it detects eating activity would be useful.

Research may seek data with respect to when individuals eat. Someresearchers study eating with a focus on the high prevalence of obesityand associated conditions, such as diabetes. Other researchers may focuson eating as a behavior of interest in itself. While a weight loss orglucose management program may seek to make individuals more aware oftheir eating, some researchers may wish to observe when individuals eatwhen they are not aware, or have forgotten, that they are beingmonitored. In such cases, a non-invasive method that attracts littleattention from the monitored individual would be ideal. As with othertechnologies, cost and ease of use are important considerations forcreating viable methods. A simple and inexpensive way to detect eatingactivity would therefore be useful to many.

Existing methods of detecting food consumption include the use ofcameras directed towards food to identify food items in the image fromthe camera. Another visual means uses visual sensors to detect thepassage of food through the esophagus. Passage of food through theesophagus may also be determined by ultrasonic sensors that detectchanges in esophageal density. Some methods determine eating byanalyzing microphone signals for eating sounds. Such methods may employthroat-mounted microphones or be subject to background noise.

One approach seeks to detect eating activity in signals from anaccelerometer. This method seeks to detect lulls in activity betweenpeaks attributable to eating activity preparation events or eatingactivity cleanup events. It does not specify how training data for itsdetection algorithm would be collected. Further, while it contemplatespopulation-based models whose parameters may be calibrated for anindividual subject, it does not disclose models created for anindividual from data specific to the individual. Thus, methods andsystems collecting training data for eating activity in a simple,automated, and inexpensive way would be useful to the field.

SUMMARY

Embodiments of the present application include methods for automateddetection of eating activity. A processing unit receives glucosereadings and a first set of acceleration readings generated during afirst time period. The glucose readings correspond to glucose levels ofan individual and the acceleration readings correspond to motion ofeither of the individual's hands. The processing unit identifies aneating episode if the glucose readings satisfy one or more criteria.Using one or more identified eating episodes and the accelerationreadings, the processing unit then generates an individual model. Theindividual model identifies eating episodes using acceleration readingsbut not using glucose readings. A processing unit (either the sameprocessing unit used during the first time period or a second processingunit) uses the individual model and a second set of accelerationreadings (generated during a second time period and corresponding tomotion of the individual's hand) to identify a second eating episode.Some embodiments may use additional types of data (for example, PPG orheart rate data) to identify eating episodes in the first time period,generate the individual model, identify eating episodes in the secondtime period, or any combination thereof.

Embodiments include systems for automated eating detection. Systems mayinclude a continuous glucose monitor (CGM), a processing unit, and anaccelerometer. The CGM and accelerometer are in communication with theprocessing unit. The processing unit identifies one or more eatingepisodes during a first time period using at least the glucose readingsgenerated by the CGM. The processing unit generates an individual modelpredicting eating episodes using readings from the accelerometer but notusing readings from the CGM. The processing unit uses the individualmodel to identify eating episodes in acceleration readings it receivesfrom the accelerometer during a second time period. Some embodiments mayuse multiple processing units or multiple accelerometers to performdifferent steps of the method. Some embodiments may use additionalsensor types (for example, PPG or heart rate sensors) to identify eatingepisodes in the first time period, generate the individual model,identify eating episodes in the second time period, or any combinationthereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 presents a flowchart illustrating a method for detecting eatingactivity, according to certain embodiments of the present disclosure.

FIG. 2 presents a flowchart illustrating a method for detecting eatingactivity including steps of coupling a CGM and an accelerometer to anindividual, according to certain embodiments of the present disclosure.

FIG. 3 presents a flowchart illustrating a method for detecting eatingactivity using first and second processing units, according to certainembodiments of the present disclosure.

FIG. 4 presents a flowchart illustrating a method for detecting eatingactivity using a single processing unit, according to certainembodiments of the present disclosure.

FIG. 5 presents a flowchart illustrating a method for detecting eatingactivity using a population-based eating movement model and glucosereadings to identify a first eating episode, according to certainembodiments of the present disclosure.

FIG. 6 presents a flowchart illustrating a method for detecting eatingactivity using glucose readings and PPG readings to determine a firsteating episode, according to certain embodiments of the presentdisclosure.

FIG. 7 presents a flowchart illustrating a method for detecting eatingactivity using acceleration readings and PPG readings to generate anindividual eating model, according to certain embodiments of the presentdisclosure.

FIG. 8 presents a flowchart illustrating a method for detecting eatingactivity using acceleration readings and PPG readings to identify asecond eating episode, according to certain embodiments of the presentdisclosure.

FIG. 9 presents a flowchart illustrating a method for detecting eatingactivity using glucose readings and heart rate readings to determine afirst eating episode, according to certain embodiments of the presentdisclosure.

FIG. 10 presents a flowchart illustrating a method for detecting eatingactivity using acceleration readings and heart rate readings to generatean individual eating model, according to certain embodiments of thepresent disclosure.

FIG. 11 presents a flowchart illustrating a method for detecting eatingactivity using acceleration readings and heart rate readings to identifya second eating episode, according to certain embodiments of the presentdisclosure.

FIG. 12 presents a flowchart illustrating a method for detecting eatingactivity in which a message is sent to a user interface if a secondeating episode is identified, according to certain embodiments of thepresent disclosure.

FIG. 13 presents a flowchart illustrating a method for detecting eatingactivity using a long-short term memory function and time-distributed,layer recursive, neural network classifier, according to certainembodiments of the present disclosure.

FIG. 14 presents a flowchart illustrating a method for detecting eatingactivity using a hidden Markov model to identify hand-to-mouthmovements, according to certain embodiments of the present disclosure.

FIG. 15 presents a diagram depicting an embodiment of a continuousglucose monitor (CGM), according to certain embodiments of the presentdisclosure.

FIG. 16 presents a diagram depicting an embodiment of a wrist-worndevice for detecting eating episodes comprising an accelerometer,according to certain embodiments of the present disclosure.

FIG. 17 presents a schematic diagram depicting an embodiment of a systemfor detecting eating episodes comprising a glucose sensor, a processingunit, and an accelerometer, according to certain embodiments of thepresent disclosure.

FIG. 18 presents a schematic diagram depicting an embodiment of a systemfor detecting eating episodes comprising a glucose sensor, twoprocessing units, and two accelerometers, according to certainembodiments of the present disclosure.

FIG. 19 presents a schematic diagram depicting an embodiment of a systemfor detecting eating episodes comprising a PPG sensor, a glucose sensor,a processing unit, and an accelerometer, according to certainembodiments of the present disclosure.

FIG. 20 presents a schematic diagram depicting an embodiment of a systemfor detecting eating episodes comprising a glucose sensor, twoprocessing units, two accelerometers, and two PPG sensors, according tocertain embodiments of the present disclosure.

FIG. 21 presents a schematic diagram depicting an embodiment of a systemfor detecting eating episodes comprising a heart rate sensor, a glucosesensor, a processing unit, and an accelerometer, according to certainembodiments of the present disclosure.

FIG. 22 presents a schematic diagram depicting an embodiment of a systemfor detecting eating episodes comprising a glucose sensor, twoprocessing units, two accelerometers, and two heart rate sensors,according to certain embodiments of the present disclosure.

DETAILED DESCRIPTION

Methods

Embodiments of the present disclosure include methods for detectingeating activity using glucose readings and acceleration readings. Aglucose reading may refer to any information or signal that indicates,or may be processed to indicate, a glucose concentration in anindividual. Glucose concentrations may be referred to as glucose levels.Glucose readings may be electronic and be either digital or analog inform. Glucose levels may be of the blood, interstitial fluid, or otherfluid or tissue of a body. Traditional finger-stick methods measureglucose levels in blood from a puncture wound and provide a glucosereading based on photometric measurement of blood on a test strip. Morerecently, continuous glucose monitors (CGMs) have been developed tomeasure glucose levels more frequently than was previously practical.For example, CGM devices may provide glucose readings every five to tenminutes. In addition to a component representing glucose concentration,a glucose reading may also include information as to the time at whichthe glucose level was measured.

An acceleration reading may refer to any information or signal thatindicates, or may be processed to indicate, an amount of acceleration.The acceleration may be linear or angular. An acceleration reading mayinclude information about acceleration along or around more than oneaxis. For example, a triaxial acceleration reading may indicateacceleration along or around three orthogonal (or approximatelyorthogonal) axes at a given time. Alternatively, acceleration along oraround the three orthogonal axes may be indicated by three separateacceleration readings. In addition to a component representing aquantity of acceleration, an acceleration reading may also includeinformation as to the direction of the acceleration and/or the time atwhich the acceleration value was generated.

A predictive model (or simply “model”) may refer to any algorithm ormathematical expression used to predict a value. A model may predict afuture, past, or present state that is not known. Some models are basedon statistical methods. Machine learning may incorporate statistical andother mathematical techniques to improve predictions without additionalprogramming. Machine learning may be supervised or unsupervised.Unsupervised techniques start with no examples outputs assigned toinputs. Supervised techniques seek to determine rules that providecorrect outputs based on inputs. Supervised techniques require atraining data set (a set of example inputs each paired with a desiredoutput) from which to learn.

Supervised machine learning techniques may be used to predict eatingactivity provided an adequate training data set. Collecting adequatetraining data has proven to be a challenge. Methods requiring anobserver recording onset and end of eating activity may be expensive andarduous even to obtain small quantities of data. Requiring individualsto record their own eating activity is problematic for reasons notedabove. Data from CGMs may be used as an indicator of eating activity andused to generate training data to detect eating activity using othersensor types. However, CGM devices currently cost more than other typesof biosensors and using CGMs for long term monitoring may beprohibitively expensive. Additionally, using devices less invasive thanCGMs may also be more practical to detect eating activity over longertime periods due to higher utilization compliance by monitoredindividuals. It may therefore be advantageous to use CGMs for a limitedtime to collect training data to create models allowing other types ofbiosensors to detect eating activity.

When training data on eating activity can be efficiently collected inlarge enough quantities, creating training data sets specific toindividuals (rather than populations) becomes feasible. Preferredembodiments of the present disclosure generate models based on CGMtraining data specific to a single individual to identify patterns ofeating activity specific to that individual.

A computer processing unit (or, simply “processing unit”) may compriseone or more computer processors and any memory necessary to processdata. If a processing unit comprises multiple processors, thoseprocessors may or may not be locate in close physical proximity. Aprocessing unit may receive glucose readings generated by a CGM or otherglucose sensor. The processing unit may identify an episode of eatingactivity when one or more glucose readings satisfy one or more glucosecriteria. Examples of glucose criteria include: (1) at least one of theglucose readings exceeding a threshold glucose level; (2) a rate ofglucose change exceeding a threshold rate of glucose change; or (3) aderivative of the rate of glucose change exceeding a threshold for thederivative of the rate of glucose change. The one or more glucosecriteria may include criteria as to time, duration, and othercontemporaneous conditions.

Once the processing unit has identified one or more eating episodes, itmay then apply various machine learning or other modeling techniques toidentify patterns in data from a second sensor that are predictive ofthe one or more eating episodes. Accelerometers are a second sensor inpreferred embodiments. Once the processing unit generates an individualmodel of eating movement, it, or another processing unit, may thenidentify one or more eating episodes using acceleration readings withoutusing glucose readings as an input.

FIG. 1 presents a flowchart illustrating a method (100) for detectingeating activity, according to certain embodiments of the presentdisclosure. After the start of a first time period (101), a processingunit determines whether glucose readings (115) generated during thefirst time period satisfy one or more glucose criteria (120). If not,processing repeats step 120 to determine whether subsequent glucosereadings satisfy the one or more glucose criteria. If the glucosereadings satisfy the one or more glucose criteria, the processing unitidentifies a first eating episode (140). Using a first set ofacceleration readings (145) generated during the first time period, theprocessing unit generates an individual model predicting eating episodesusing at least the first eating episode as training data (150). Theindividual model predicts eating episodes using acceleration readings.Those familiar with the art will understand that multiple identifiedeating episodes in addition to the first eating episode may be used astraining data to generate the individual model. The individual model maybecome more robust as more identified eating episodes are included inthe training data set. Collection of training data ceases at the end ofthe first time period (159). A second time period begins at 161. Aprocessing unit (either the same used in the first time period or asecond processing unit) applies the individual model generated at 150 toa second set of acceleration readings (165) generated during the secondtime period to determine whether eating activity is occurring (170). Ifthe individual model indicates no activity, processing repeats step 170to monitor subsequent acceleration readings for eating activity. Ifeating activity is detected, the processing unit identifies a secondeating episode (180) and ends (199).

Not all accelerometer data is equally predictive. Most individuals eatusing their hands (e.g. to manipulate utensils for eating). Someembodiments place the accelerometer at a body location whose movement isindicative of movement of one of the individual's hands, particularlythe individual's dominant hand or the hand the individual typically usesto eat. For example, the accelerometer may be coupled the wrist,forearm, or hand itself.

FIG. 2 presents a flowchart illustrating a method (200) for detectingeating activity including steps of coupling a CGM and an accelerometerto an individual, according to certain embodiments of the presentdisclosure. A first time period begins at 101. An accelerometer iscoupled to a wrist, hand, or forearm of an individual (212). Preferredembodiments may couple the accelerometer to the individual's dominantside or the side the individual typically uses to eat. A CGM is coupledto the individual allowing the CGM to generate glucose readingscorresponding to glucose levels of the individual (214). A processingunit then determines whether the glucose readings (115) generated by theCGM during the first time period satisfy one or more glucose criteria(120). If not, processing repeats step 120 to determine whethersubsequent glucose readings satisfy the one or more glucose criteria. Ifthe glucose readings satisfy the one or more glucose criteria, theprocessing unit identifies a first eating episode (140). Using a firstset of acceleration readings (145) generated during the first timeperiod, the processing unit generates an individual model predictingeating episodes using at least the first eating episode as training data(150). The individual model identifies eating episodes in accelerationreadings. Collection of training data ceases at the end of the firsttime period (159). The individual model may then be used to identifyeating episodes using acceleration readings during a second time period,as described in FIG. 1 and elsewhere herein.

FIG. 3 presents a flowchart illustrating a method (300) for detectingeating activity using first and second processing units, according tocertain embodiments of the present disclosure. The steps of FIG. 3 arethe same as those in FIG. 1. Steps 120 (determining whether glucosereadings satisfy one or more criteria), 140 (identifying a first eatingepisode), and 150 (generating an individual model) are performed at afirst processing unit (310). Steps 170 (identifying eating using theindividual model) and 180 (identifying a second eating episode) areperformed on a second processing unit (360). Those familiar with the artwill understand that the steps of the method (300) could each beperformed by a single processing unit or that multiple processing unitscould perform any combination of the steps. For example, identifying thefirst eating episode (140) could be performed by a different processingunit than the step of generating the individual model (150). Given thata processing unit may comprise more than one processor and that multipleprocessors within a processing unit need not be located in closeproximity with one another, distinctions among which steps are performedby which processors are largely immaterial in the context of the method.

FIG. 4 presents a flowchart illustrating a method (400) for detectingeating activity using a single processing unit, according to certainembodiments of the present disclosure. FIG. 4 is identical to FIG. 3except that all steps are performed by a single processing unit (460).Again, this figure illustrates that distinctions among which steps areperformed by which processors are largely immaterial in the context ofthe method.

Eating is not the only activity that can cause glucose levels toincrease. For example, gluconeogenesis (a physiological process in whichglucose may be synthesized from lipids or proteins) can cause glucoselevels to increase. Breakdown of glycogen can also increase glucoselevels. How can increases in glucose levels caused by eating activity bedistinguished from other causes? Glucose signals showing increasescaused by eating may have different characteristics than those withother causes. In this case, eating may still be determined from glucosereadings alone. Alternatively, input from a second sensor may helpdistinguish eating activity from other glucose increases.Population-based models of eating movement provide one means fordistinguishing eating activity. Such population-based models may beapplied to acceleration data to determine potential eating episodes. Ifan increase in glucose levels occurs within a short time of when apopulation-based model indicates a potential eating episode, it may bereasonable to infer that eating activity caused the glucose increase.However, if no eating motions occur near the time of glucose increase,the increase may be due to non-eating factors. This may especially bethe case when non-eating motion patterns, such as physical exercise, areidentified in acceleration readings. Conversely, identification by thepopulation-based model of a potential eating episode that is notaccompanied by an increase in glucose may be caused by motions that arenot associated with eating. The population-based model may be used toidentify eating episodes in training data so that an individual(non-population-based) model may be generated for use after the trainingperiod.

FIG. 5 presents a flowchart illustrating a method (500) for detectingeating activity using a population-based eating movement model andglucose readings to identify a first eating episode, according tocertain embodiments of the present disclosure. A first time periodbegins at 101. A processing unit applies a population-based eatingmovement model to a first set of acceleration readings (145) todetermine whether there is a potential eating episode (510). If not,processing repeats step 510 to determine whether subsequent accelerationreadings indicate a potential eating episode according to thepopulation-based model. If the population-based model indicates apotential eating episode, the processing unit then determines whetherthe glucose readings (115) generated during the first time periodsatisfy one or more glucose criteria within a threshold amount of timefrom the potential eating episode (520). If not, processing returns tostep 510 to determine whether subsequent acceleration readings indicatea potential eating episode according to the population-based model. Ifthe glucose readings satisfy the one or more glucose criteria within thethreshold amount of time, the processing unit identifies a first eatingepisode (140). Using the first set of acceleration readings (145)generated during the first time period, the processing unit generates anindividual model predicting eating episodes using at least the firsteating episode as training data (150). The individual model identifieseating episodes using acceleration readings. Collection of training dataceases at the end of the first time period (159). The individual modelmay then be used to identify eating episodes using acceleration readingsduring a second time period, as described in FIG. 1 and elsewhereherein.

A photo-plethysmograph (PPG) is a second sensor type that may be used toidentify eating episodes. While plethysmography refers generally to themeasurement of volume, PPG has many applications beyond volumemeasurements. PPG devices measure either absorbance or transmission oflight, especially light in the infrared (IR) part of the spectrum.Because blood absorbs IR light to a greater degree than other tissues,the intensity of absorbed or transmitted IR light may be proportional tothe amount of blood in blood vessels, especially the capillaries andother microvascular tissue. This proportionality may be distinguishedfrom an absolute volume measurement. Rapid changes in IR absorbance maybe attributed to blood volume changes caused by heart beats. In additionto measuring blood flow, PPG may be used to measure blood oxygensaturation due to the difference in IR absorbance between oxygenated andnon-oxygenated blood. PPG may be used to determine other physiologicalmeasures as well. Thus, as used herein, a PPG reading may refer to anymeasurement of light absorbance or transmission from a body or to anyparticular measure derived from changes in absorbance or transmission.

PPG readings may be used to increase predictive accuracy at variouspoints in the method. For example, PPG readings may be used in additionto glucose readings to identify one or more eating episodes for atraining data set. PPG readings may be used in addition to accelerationreadings generated in a training period to generate an individual model.PPG may also be used in addition to acceleration readings to identifyeating episodes after the training period has ended. These scenarios areillustrated in the next three figures.

FIG. 6 presents a flowchart illustrating a method (600) for detectingeating activity using glucose readings and PPG readings to determine afirst eating episode, according to certain embodiments of the presentdisclosure. After the start of a first time period (101), a processingunit determines whether glucose readings (115) generated during thefirst time period satisfy one or more glucose criteria (120). If not,processing repeats step 120 to determine whether subsequent glucosereadings satisfy the one or more glucose criteria. If the glucosereadings satisfy the one or more glucose criteria, the processing unitdetermines whether PPG readings (625) generated during the first timeperiod satisfy one or more PPG criteria (630). If not, processingreturns to step 120 to determine whether glucose readings satisfy theone or more glucose criteria. If the PPG readings satisfy the one ormore PPG criteria, the processing unit identifies a first eating episode(140). Using a first set of acceleration readings (145) generated duringthe first time period, the processing unit generates an individual modelpredicting eating episodes using at least the first eating episode astraining data (150). The individual model identifies eating episodes inacceleration readings. Collection of training data ceases at the end ofthe first time period (159). The individual model may then be used toidentify eating episodes using acceleration readings during a secondtime period, as described in FIG. 1 and elsewhere herein.

FIG. 7 presents a flowchart illustrating a method (700) for detectingeating activity using acceleration readings and PPG readings to generatean individual eating model, according to certain embodiments of thepresent disclosure. After the start of a first time period (101), a PPGsensor is coupled to an individual such that it measures PPG readingsfrom some part of the individual's body (710). A processing unitdetermines whether glucose readings (115) generated during the firsttime period satisfy one or more glucose criteria (120). If not,processing repeats step 120 to determine whether subsequent glucosereadings satisfy the one or more glucose criteria. If the glucosereadings satisfy the one or more glucose criteria, the processing unitidentifies a first eating episode (140). Using a first set ofacceleration readings (145) and a first set of PPG readings (725), bothsets of readings generated during the first time period, the processingunit generates an individual model predicting eating episodes using atleast the first eating episode (750). The individual model predictseating episodes using acceleration readings and PPG readings. Collectionof training data ceases at the end of the first time period (159). Theindividual model may then be used to identify eating episodes usingacceleration readings and PPG readings during a second time period, asdescribed in, for example, FIG. 8 and elsewhere herein.

FIG. 8 presents a flowchart illustrating a method (800) for detectingeating activity using acceleration readings and PPG readings to identifya second eating episode, according to certain embodiments of the presentdisclosure. Method 800 begins after creation of an individual modelpredicting eating episodes using acceleration readings and PPG readingsas, for example, in FIG. 7. A second time period begins at 161. Aprocessing unit (either the same used in the first time period or asecond processing unit) applies an individual model to a second set ofacceleration readings (165) and a second set of PPG readings (865), bothsets of readings generated during the second time period, to determinewhether there is eating activity (870). If the individual modelindicates no activity, processing repeats step 870 to monitoracceleration readings and PPG readings for eating activity. If eatingactivity is detected, the processing unit identifies a second eatingepisode (180). Processing then determines whether to continue monitoringfor additional eating episodes (890). If additional monitoring isindicated, processing returns to step 870. If no further monitoringoccurs, the method (800) ends (199).

A heart rate sensor may refer to any device or component that detectsheart beats. Two types of sensor are commonly used. One is PPG, whichdetects light transmission or absorbance, as discussed herein.Electrocardiography (ECG) is another method of detecting heart beats.ECG detects electrical pulses from the cells that initiate heart beats.The electrical pulses are detected by electrodes which may contact theskin. Electrodes placed directly over the heart may receive thestrongest electrical activity, but electrodes at more remote locations(e.g. wrist, hands, or fingers) may be able to detect cardiac electricalactivity. A heart rate reading may refer to any output of a heart ratesensor. A heart rate reading may correspond to a single heart beat or amultiple heart beats. A heart rate reading may correspond to a period oftime in which no heart beats are detected. A heart rate reading may bedigital or analog and correspond either some count of heart beats orsome output from which a count of heart beats may be derived.

FIG. 9 presents a flowchart illustrating a method (900) for detectingeating activity using glucose readings and PPG readings to determine afirst eating episode, according to certain embodiments of the presentdisclosure. After the start of a first time period (101), a processingunit determines whether glucose readings (115) generated during thefirst time period satisfy one or more glucose criteria (120). If not,processing repeats step 120 to determine whether subsequent glucosereadings satisfy the one or more glucose criteria. If the glucosereadings satisfy the one or more glucose criteria, the processing unitdetermines whether heart rate readings (925) generated during the firsttime period satisfy one or more heart rate criteria (930). If not,processing returns to step 120 to determine whether glucose readingssatisfy the one or more glucose criteria. If the heart rate readingssatisfy the one or more heart rate criteria, the processing unitidentifies a first eating episode (140). Using a first set ofacceleration readings (145) generated during the first time period, theprocessing unit generates an individual model predicting eating episodesusing at least the first eating episode as training data (150). Theindividual model identifies eating episodes using acceleration readings.Collection of training data ceases at the end of the first time period(159). The individual model may then be used to identify eating episodesusing acceleration readings during a second time period, as described inFIG. 1 and elsewhere herein.

FIG. 10 presents a flowchart illustrating a method (1000) for detectingeating activity using acceleration readings and heart rate readings togenerate an individual eating model, according to certain embodiments ofthe present disclosure. After the start of a first time period (101), aheart rate sensor is coupled to an individual (1010). A processing unitdetermines whether glucose readings (115) generated during the firsttime period satisfy one or more glucose criteria (120). If not,processing repeats step 120 to determine whether subsequent glucosereadings satisfy the one or more glucose criteria. If the glucosereadings satisfy the one or more glucose criteria, the processing unitidentifies a first eating episode (140). Using a first set ofacceleration readings (145) and a first set of heart rate readings(1025), both sets of readings generated during the first time period,the processing unit generates an individual model predicting eatingepisodes using at least the first eating episode as training data(1050). The individual model predicts eating episodes using accelerationreadings and heart rate readings. Collection of training data ceases atthe end of the first time period (159). The individual model may then beused to identify eating episodes using acceleration readings and heartrate readings during a second time period, as described in, for example,FIG. 11 and elsewhere herein.

FIG. 11 presents a flowchart illustrating a method (1100) for detectingeating activity using acceleration readings and heart rate readings toidentify a second eating episode, according to certain embodiments ofthe present disclosure. Method 1100 begins after creation of anindividual model predicting eating episodes using acceleration readingsand heart rate readings as, for example, in FIG. 10. A second timeperiod begins at 161. A processing unit (either the same used in thefirst time period or a second processing unit) applies an individualmodel to a second set of acceleration readings (165) and a second set ofheart rate readings (1165), both sets of readings generated during thesecond time period, to determine if there is eating activity (1170). Ifthe individual model indicates no activity, processing repeats step 1170to monitor subsequent acceleration readings and PPG readings for eatingactivity. If eating activity is detected, the processing unit identifiesa second eating episode (180). Processing then determines whether tocontinue monitoring for additional eating episodes (890). If additionalmonitoring is indicated, processing returns to step 1170. If no furthermonitoring is indicated, the method (1100) ends (199).

Detecting eating may be useful as an end in itself but may also be usedas a means to accomplish other ends. For example, automated eatingdetection may be used as part of a program to influence eating behaviorsthat could be used to achieve ends such as weight loss or control ofglucose levels. For example, if eating is detected a message may be sentto a user interface indicating that the recipient of the message, or anindividual the recipient is coaching, eat or avoid particular types offood or limit the quantity of food eaten. In another embodiment, ifacceleration readings indicate a threshold quantity of physicalactivity, but do not indicate eating for a threshold amount of time, theprocessing unit may send a message to a user interface indicating thatit is time for the recipient or coached individual to eat in order toavoid hypoglycemia.

FIG. 12 presents a flowchart illustrating a method (1200) for detectingeating activity in which a message is sent to a user interface if asecond eating episode is identified, according to certain embodiments ofthe present disclosure. Method 1200 begins after creation of anindividual model predicting eating episodes using acceleration readingsand heart rate readings as, for example, in FIG. 1. A second time periodbegins at 161. A processing unit (either the same used in the first timeperiod or a second processing unit) applies the individual model to asecond set of acceleration readings (165) generated during the secondtime period to determine whether eating activity is occurring (170). Ifthe individual model indicates no activity, processing repeats step 170to monitor acceleration readings for eating activity. If eating activityis detected, the processing unit identifies a second eating episode(180) and the processing unit sends a message to a user interfacerecommending a behavior that influences glucose levels (1290). Themethod (1200) ends at 199.

Machine learning encompasses a great variety of techniques. For anyparticular prediction task, some machine learning techniques may be moreeffective than others. In addition, there are multiple approaches toidentifying eating activity from acceleration readings. Some approachesare binary, seeking to distinguish only between eating activity and anyactivity that is not eating. In particular, these approaches may seek todistinguish hand-to-mouth gestures from non-hand-to-mouth activity.Other approaches identify eating activity in addition to a number ofother activity types. For example, a machine learning model may identifyactivity states for physical exercise, tooth brushing, and piano playingin addition to eating.

Applicant has experimented with a number of different machine learningtechniques and approaches to classifying acceleration data and foundsome to work particularly well. Hidden Markov models (HMMs) provedparticularly effective at distinguishing hand-to-mouth gestures fromnon-hand-to-mouth activity. For approaches distinguishing multipleactivity states, long short term memory (LSTM) functions used with atime-distributed layer-recursive neural network classifier (TDLRNNC)yielded results superior to other methods. Use of a two-stacked LSTMproved particularly effective. Contrary to what intuition might suggest,approaches identifying multiple activity states in addition to eatingproved more accurate than binary eating/non-eating models outside of thelaboratory setting. The Appendix attached hereto displays code used togenerate individual models using HMM, LSTM, and neural networks.

FIG. 13 presents a flowchart illustrating a method (1300) for detectingeating activity using a hidden Markov model (HMM) to identifyhand-to-mouth movements, according to certain embodiments of the presentdisclosure. After the start of a first time period (101), a processingunit determines whether glucose readings (115) generated during thefirst time period satisfy one or more glucose criteria (120). If not,processing repeats step 120 to determine whether subsequent glucosereadings satisfy the one or more glucose criteria. If the glucosereadings satisfy the one or more glucose criteria, the processing unitdetermines whether a first set of acceleration readings (145) generatedduring the first time period is characteristic of hand-to-mouthmovements (1330). If not, processing returns to step 120 to determinewhether subsequent glucose readings satisfy the one or more glucosecriteria. If the acceleration readings (145) are characteristic ofhand-to-mouth movements, the processing unit identifies a first eatingepisode (140). Using the first set of acceleration readings (145), theprocessing unit generates an individual model comprising a hidden Markovmodel (HMM) predicting eating episodes using at least the first eatingepisode as training data (1350). The individual model identifieshand-to-mouth gestures in acceleration readings using the HMM.Collection of training data ceases at the end of the first time period(159). The individual model may then be used to identify eating episodesusing acceleration readings during a second time period, as described inFIG. 1 and elsewhere herein.

FIG. 14 presents a flowchart illustrating a method (1400) for detectingeating activity among multiple activity types using a long-short termmemory (LSTM) function and a time-distributed, layer recursive, neuralnetwork classifier (TDLRNNC), according to certain embodiments of thepresent disclosure. After the start of a first time period (101), aprocessing unit determines whether glucose readings (115) generatedduring the first time period satisfy one or more glucose criteria (120).If not, processing repeats step 120 to determine whether subsequentglucose readings satisfy the one or more glucose criteria. If theglucose readings satisfy the one or more glucose criteria, theprocessing unit determines whether a first set of acceleration readings(145) generated during the first time period is characteristic of two ormore non-eating activity states (1433). If so, processing returns tostep 120 to determine whether subsequent glucose readings satisfy theone or more glucose criteria. If the first set of acceleration readings(145) are not characteristic of two or more non-eating states, theprocessing unit determines whether the first set of accelerationreadings (145) are characteristic of an eating state (1437). If not,processing returns to step 120 to determine whether subsequent glucosereadings satisfy the one or more glucose criteria. If the first set ofacceleration readings (145) are characteristic of an eating state, theprocessing unit identifies a first eating episode (140). Using the firstset of acceleration readings (145), the processing unit generates anindividual model comprising a long-short term memory (LSTM) function anda time-distributed, layer recursive, neural network classifier (TDLRNNC)(1450). The individual model predicts eating episodes using at least thefirst eating episode as training data. The individual model identifiesmovements characteristic of eating using the LSTM function and TDLRNNC.Collection of training data ceases at the end of the first time period(159). The individual model may then be used to identify eating episodesusing acceleration readings during a second time period, as described inFIG. 1 and elsewhere herein.

Systems:

System embodiments may include a glucose sensor and an accelerometercommunicatively coupled to a processing unit. The processing unit may,optionally, be communicatively coupled to a user interface. Systemcomponents may be housed in a single device or be distributed amongmultiple devices. Communicative coupling does not necessarily require aphysical connection and indicates only that one system component maysend or receive information to or from another. If one component iscommunicatively coupled to another, it may be referred to as being incommunication with the other component. One component may becommunicatively coupled to another if, for example, it sends or receiveselectromagnetic transmissions to or from the other component.

A glucose sensor may refer to any device or component that measuresglucose levels. The output of the monitor may be analog, digital, or ofother format and may or may not require other devices or components toconvert the output of the glucose sensor into a glucose level. Glucosesensors may include, for example, those in direct contact with blood orother bodily fluids or tissues or those measuring glucose without directcontact including transmission and reflection spectroscopy. Continuousglucose monitoring (CGM) includes a variety of devices and techniquesthat measure glucose more frequently than was practical with earliermethods. In the context of CGM, continuous does not require thatreadings are either instantaneous or absolutely continuous. For example,CGM devices may provide measurements every five to ten minutes.

FIG. 15 presents a diagram depicting an embodiment of a continuousglucose monitor (CGM). The CGM (1500) comprises a housing (1510), asensor wire (1520), detection electronics (1530), and a transmitter(1540). The sensor wire (1520) traverses the skin (1550) so that itcomes in contact with interstitial fluid (1560). Glucose molecules(1570) in the blood stream (1580) pass through the wall of a bloodvessel (1585) and into the interstitial fluid (1560). Once in theinterstitial fluid (1560), glucose molecules (1570) may be absorbed bycells (1590) in the interstitial fluid or come in contact with thesensor wire (1520). The detection electronics (1530) detect theconcentration of glucose (1570) in contact with the sensor wire (1520)and relay glucose to the transmitter (1540). The transmitter (1540) thentransmits the glucose readings over radio frequencies.

An accelerometer may refer to any device that measures either linear orangular acceleration. However, accelerometers measuring angularacceleration may also be referred to as gyroscopes, gyrometers, orsimply gyros. An accelerometer may also refer to a device that measuresacceleration in more than one direction and/or that measures both linearand angular acceleration. Devices referred to as nine-axisaccelerometers measure both linear and angular acceleration along oraround (respectively) three orthogonal axes as well as orientation ofthe accelerometer relative to magnetic fields such as that of the Earth.The axes of accelerometers with multiple axes may be orthogonal orapproximately orthogonal. Acceleration readings may be used to estimatederived quantities of physical activity such as step count, caloriesburned, or distance traveled.

FIG. 16 presents a diagram depicting an embodiment of a wearable device(1600) for detecting eating episodes. The device (1600) comprises adevice housing (1610) and a strap (1620). An accelerometer (not visible)may be located in the device housing (1610). The strap (1620) may beused to secure the device housing (1610) to an individual, for exampleby placing the strap (1620) around the wrist, hand, or forearm. Twobuttons (1630, 1640) protrude from the side of the device housing (1610)and may be used to, for example, turn the device on and off, transitionamong different functions of the device, or to signal the beginning orend of an eating episode. The device (1600) comprises a display screen(1650) visible at a top surface of the device (1600). The display screen(1650) comprises a description of the current state (1653) as determinedusing acceleration readings from the accelerometer. In this particularillustration, the description of the state (1653) is identified as beingan eating event. Other potential states may include a non-eating state,or other activity states such as physical exercise, sleep, or brushingteeth. The display screen (1650) also comprises a duration (1655) of thecurrent state. The display screen (1650) further comprises a graph(1657) of activity level which may be indicative of an activity state ofthe individual wearing the device (1600). A device such as device 1600may comprise a processing unit configured to apply an individual modelof eating motions to motions detected by the accelerometer housedwithin. The device (1600) may optionally comprise a transmitter (notvisible) to communicate acceleration readings to an external processingunit for purposes of generating an individual model of eating. Thedevice (1600) may optionally comprise a PPG sensor or a heart ratesensor (not visible).

The glucose monitor and accelerometer may be communicatively coupled toa processing unit configured, though design or programming, to processoutputs from the respective sensor types. Processing units may compriseone or more processors and any memory or other data storage necessary toprocess and store data. A processing unit may store instructionsexecuted by the one or more processors. If a processing unit comprisesmore than one processor, the multiple processors need not be located inclose physical proximity to one another. Therefore, steps of methodsperformed on a single processing unit may be performed by multipleprocessors at multiple locations and multiple times.

FIG. 17 presents a schematic diagram depicting an embodiment of a system(1700) for detecting eating episodes. The system (1700) comprises aglucose sensor (1720) and an accelerometer (1730), each communicativelycoupled to a processing unit (460), according to the present disclosure.The dashed lines connecting the components indicate that they arecommunicatively coupled; information may be transferred from one toanother, but does not necessarily require a physical connection. Theprocessing unit (460) and accelerometer (1730) may be housed in a singledevice (1780) such as, for example, that illustrated in FIG. 16. Theglucose sensor may be part of a CGM device such as, for example, thatillustrated in FIG. 15. The processing unit (460) receives a set ofglucose readings generated by the glucose sensor (1720) during a firsttime period and a first set of acceleration readings generated by theaccelerometer (1730) during the first time period. The processing unit(460) identifies a first eating episode if the set of glucose readingssatisfies one or more glucose criteria and generates an individual modelusing the first set of acceleration readings and the first eatingepisode. The individual model uses acceleration readings correspondingto motion of the hand of the individual to identify eating episodes anddoes not use glucose readings. The processing unit (460) receives asecond set of acceleration readings generated by the accelerometer(1730) during a second time period and identifies a second eatingepisode using the individual model and the second set of accelerationreadings.

As discussed herein, methods may be performed by one or more processingunits to detect eating episodes. Similarly, methods may use a singleaccelerometer during both the training and subsequent detection periods,or use multiple accelerometers during these periods.

FIG. 18 presents a schematic diagram depicting an embodiment of a system(1800) for detecting eating episodes comprising two processing units(310, 360), a glucose sensor (1720), and two accelerometers (1830,1870), according to the present disclosure. The glucose sensor (1720)and first accelerometer (1830) are each communicatively coupled to thefirst processing unit (310). The dashed lines connecting the componentsindicate that they are communicatively coupled; information may betransferred from one to another, but does not necessarily require aphysical connection. The first processing unit may be housed, forexample, in a personal computer. The second accelerometer (1870) iscommunicatively coupled to the second processing unit (360). The secondprocessing unit (360) and second accelerometer (1870) may be housed in asingle device (1780) such as, for example, that illustrated in FIG. 16.The first processing unit (310) is communicatively coupled to the secondprocessing unit (360) at least for purposes of communicating a personalmodel of eating motion. The first processing unit (310) receives a setof glucose readings generated by the glucose sensor (1720) during afirst time period and a first set of acceleration readings generated bythe first accelerometer (1830) during the first time period. The firstprocessing unit (310) identifies a first eating episode if the set ofglucose readings satisfies one or more glucose criteria and generates anindividual model using the first set of acceleration readings and thefirst eating episode. The individual model uses acceleration readingscorresponding to motion of the hand of the individual to identify eatingepisodes and does not use glucose readings. The first processing unit(310) communicates the individual model to the second processing unit(360). The second processing unit (360) receives a second set ofacceleration readings generated by the second accelerometer (1870)during a second time period and identifies a second eating episode usingthe individual model and the second set of acceleration readings.

Some methods use PPG readings to add predictive value to the glucosereadings and/or acceleration readings. FIG. 19 presents a schematicdiagram depicting an embodiment of a system (1900) for detecting eatingepisodes comprising a PPG sensor (1950), a glucose sensor (1720), and anaccelerometer (1730) each communicatively coupled to a processing unit(460), according to certain embodiments of the present disclosure. Thedashed lines connecting the components indicate that they arecommunicatively coupled; information may be transferred from one toanother, but does not necessarily require a physical connection. Theprocessing unit (460), PPG sensor (1950), and accelerometer (1730) maybe housed in a single device (1980) such as, for example, thatillustrated in FIG. 16. The processing unit (460) receives: a set ofglucose readings generated by the glucose sensor (1720) during a firsttime period; a first set of acceleration readings generated by theaccelerometer (1730) during the first time period; and a first set ofPPG readings generated by the PPG sensor (1950) during the first timeperiod. The processing unit (460) identifies a first eating episode ifthe set of glucose readings satisfies one or more glucose criteria andgenerates an individual model using the first set of accelerationreadings, the first set of PPG readings, and the first eating episode.The individual model uses acceleration readings corresponding to motionof the hand of the individual and PPG readings of the individual toidentify eating episodes and does not use glucose readings. Theprocessing unit (460) receives a second set of acceleration readingsgenerated by the accelerometer (1730) during a second time period and asecond set of PPG readings generated by the PPG sensor (1950) during thesecond time period. The processing unit (460) identifies a second eatingepisode using the individual model, the second set of PPG readings, andthe second set of acceleration readings.

As discussed with processing units and accelerometers, methods involvingPPG readings may be performed using a single PPG sensor during both thetraining and subsequent detection periods, or use multiple PPG sensorsduring these periods. FIG. 20 presents a schematic diagram depicting anembodiment of a system for detecting eating episodes comprising aglucose sensor (1720), two processing units (310, 360), twoaccelerometers (1830, 1870), and two PPG sensors (2051, 2052), accordingto certain embodiments of the present disclosure. The glucose sensor(1720), first accelerometer (1830), and first PPG sensor (2051) are eachcommunicatively coupled to the first processing unit (310). The dashedlines connecting the components indicate that they are communicativelycoupled; information may be transferred from one to another, but doesnot necessarily require a physical connection. The first PPG sensor(2051) and first accelerometer (1870) may be housed in a single device(2081) such as, for example, that illustrated in FIG. 16. The secondaccelerometer (1870) and second PPG sensor (2052) are communicativelycoupled to the second processing unit (360). The second processing unit(360), second PPG sensor, and second accelerometer (1870) may be housedin a single device (2082) such as, for example, that illustrated in FIG.16. The first processing unit (310) is communicatively coupled to thesecond processing unit (360) at least for purposes of communicating apersonal model of eating motion. The first processing unit (310)receives a set of glucose readings generated by the glucose sensor(1720) during a first time period, a first set of acceleration readingsgenerated by the first accelerometer (1830) during the first timeperiod, and a first set of PPG readings generated by the first PPGsensor (2051) during the first time period. The first processing unit(310) identifies a first eating episode if the set of glucose readingssatisfies one or more glucose criteria and generates an individual modelusing the first set of acceleration readings, the first set of PPGreadings, and the first eating episode. The individual model usesacceleration readings corresponding to motion of the hand of theindividual and PPG readings from the individual to identify eatingepisodes and does not use glucose readings. The first processing unit(310) communicates the individual model to the second processing unit(360). The second processing unit (360) receives a second set ofacceleration readings generated by the second accelerometer (1870)during a second time period and a second set of PPG readings generatedby the second PPG sensor (2052) during the second time period. Thesecond processing unit (360) identifies a second eating episode usingthe individual model, the second set of acceleration readings, and thesecond set of PPG readings.

Some methods use heart rate readings to add predictive value to theglucose readings and/or acceleration readings. FIG. 21 presents aschematic diagram depicting an embodiment of a system (2100) fordetecting eating episodes comprising a heart rate sensor (2150), aglucose sensor (1720), and an accelerometer (1730) each communicativelycoupled to a processing unit (460), according to certain embodiments ofthe present disclosure. The dashed lines connecting the componentsindicate that they are communicatively coupled; information may betransferred from one to another, but does not necessarily require aphysical connection. The processing unit (460), heart rate sensor(2150), and accelerometer (1730) may be housed in a single device (2180)such as, for example, that illustrated in FIG. 16. The processing unit(460) receives: a set of glucose readings generated by the glucosesensor (1720) during a first time period; a first set of accelerationreadings generated by the accelerometer (1730) during the first timeperiod; and a first set of heart rate readings generated by the heartrate sensor (2150) during the first time period. The processing unit(460) identifies a first eating episode if the set of glucose readingssatisfies one or more glucose criteria and generates an individual modelusing the first set of acceleration readings, the first set of heartrate readings, and the first eating episode. The individual model usesacceleration readings corresponding to motion of the hand of theindividual and heart rate readings of the individual to identify eatingepisodes and does not use glucose readings. The processing unit (460)receives a second set of acceleration readings generated by theaccelerometer (1730) during a second time period and a second set ofheart rate readings generated by the heart rate sensor (2150) during thesecond time period. The processing unit (460) identifies a second eatingepisode using the individual model, the second set of heart ratereadings, and the second set of acceleration readings.

As discussed with processing units and accelerometers, methods involvingheart rate readings may be performed using a single heart rate sensorduring both the training and subsequent detection periods, or usemultiple heart rate sensors during these periods. FIG. 22 presents aschematic diagram depicting an embodiment of a system for detectingeating episodes comprising a glucose sensor (1720), two processing units(310, 360), two accelerometers (1830, 1870), and two heart rate sensors(2251, 2252), according to certain embodiments of the presentdisclosure. The glucose sensor (1720), first accelerometer (1830), andfirst heart rate sensor (2251) are each communicatively coupled to thefirst processing unit (310). The dashed lines connecting the componentsindicate that they are communicatively coupled; information may betransferred from one to another, but does not necessarily require aphysical connection. The first heart rate sensor (2251) and firstaccelerometer (1870) may be housed in a single device (2281) such as,for example, that illustrated in FIG. 16. The second accelerometer(1870) and second heart rate sensor (2252) are communicatively coupledto the second processing unit (360). The second processing unit (360),second heart rate sensor, and second accelerometer (1870) may be housedin a single device (2282) such as, for example, that illustrated in FIG.16. The first processing unit (310) is communicatively coupled to thesecond processing unit (360) at least for purposes of communicating apersonal model of eating motion. The first processing unit (310)receives a set of glucose readings generated by the glucose sensor(1720) during a first time period, a first set of acceleration readingsgenerated by the first accelerometer (1830) during the first timeperiod, and a first set of heart rate readings generated by the firstheart rate sensor (2251) during the first time period. The firstprocessing unit (310) identifies a first eating episode if the set ofglucose readings satisfies one or more glucose criteria and generates anindividual model using the first set of acceleration readings, the firstset of heart rate readings, and the first eating episode. The individualmodel uses acceleration readings corresponding to motion of the hand ofthe individual and heart rate readings from the individual to identifyeating episodes and does not use glucose readings. The first processingunit (310) communicates the individual model to the second processingunit (360). The second processing unit (360) receives a second set ofacceleration readings generated by the second accelerometer (1870)during a second time period and a second set of heart rate readingsgenerated by the second heart rate sensor (2252) during the second timeperiod. The second processing unit (360) identifies a second eatingepisode using the individual model, the second set of accelerationreadings, and the second set of heart rate readings.

The above description is neither exclusive nor exhaustive and does notnecessarily describe all possible embodiments (also called “examples”)nor is intended to limit the scope of the claims. Embodiments mayinclude elements in addition to those described and, in some cases, maycontain only a subset of the elements described in a particularembodiment. Embodiments may contain any combination of elements in thedescribed embodiments in addition to elements not expressly described.As used herein, the articles “a” and “an” may include one or more thanone of the noun modified by either without respect to other uses ofphrases such as “one or more” or “at least one.” The word “or” is usedinclusively unless otherwise indicated. Terms such as “first,” “second,”“third” and so forth are used as labels to distinguish elements and donot indicate sequential order unless otherwise indicated. In addition tothe embodiments described above, embodiments include any that would fallwithin the scope of the claims, below.

What is claimed is:
 1. A method for using machine learning to detecteating activity of an individual, the method comprising: receiving, byan apparatus comprising one or more processors, a set of glucosereadings, wherein (a) the set of glucose readings corresponds to glucoselevels of the individual during a first time period, (b) the set ofglucose readings is captured during the first time period by acontinuous glucose monitoring (CGM) device coupled to the individual,(c) the CGM device comprises a glucose sensor that collects CGM glucosereadings, and (d) the set of glucose readings is wirelessly transmittedto the apparatus by the CGM device; determining, by the apparatuscomprising the one or more processors, that the set of glucose readingssatisfies one or more glucose criteria indicative of a first eatingepisode; responsive to determining that the set of glucose readingssatisfies the one or more glucose criteria indicative of the firsteating episode, determining, by the apparatus comprising the one or moreprocessors, whether a set of photo-plethysmograph (PPG) readingssatisfies one or more PPG criteria indicative of the first eatingepisode, wherein (a) the set of PPG readings corresponds to blood flowduring the first time period, (b) the set of PPG readings is capturedduring the first time period by a wearable device worn by theindividual, (c) the wearable device comprises both a PPG sensor thatcollects PPG readings and an accelerometer that collects accelerometerreadings, and (d) the set of PPG readings is wirelessly transmitted tothe apparatus by the wearable device; responsive to determining that theset of PPG readings satisfies one or more PPG criteria indicative of thefirst eating episode, identifying, by the apparatus comprising the oneor more processors, a first set of acceleration readings as trainingdata for training a machine learning model configured to generatepredictions of eating episodes, wherein (a) the first set ofacceleration readings corresponds to motion of a hand of the individual,and (b) the first set of acceleration readings is captured during thefirst time period by the accelerometer of the wearable device;programmatically training, by the apparatus comprising the one or moreprocessors, the machine learning model, wherein the trained machinelearning model (a) generates predictions of eating episodes based atleast in part on acceleration readings, and (b) is stored in one or morememory storage areas accessible by the apparatus; receiving, by theapparatus comprising the one or more processors, a second set ofacceleration readings, wherein (a) the second set of accelerationreadings corresponds to motion of the hand of the individual, and (b)the second set of acceleration readings is captured during a second timeperiod by the accelerometer of the wearable device; generating, by theapparatus comprising the one or more processors and using the trainedmachine learning model and the second set of acceleration readings, aprediction that the second set of acceleration readings indicates asecond eating episode; and providing, by the apparatus comprising theone or more processors, an indication of the prediction that the secondset of acceleration readings indicates a second eating episode to thewearable device, wherein the wearable device causes display of at leasta portion of the indication.
 2. The method of claim 1, wherein the oneor more glucose criteria comprise one or more of (a) at least one of theset glucose readings exceeding a threshold glucose level, (b) a rate ofglucose change exceeding a threshold rate of glucose change, or (c) aderivative of the rate of glucose change exceeding a threshold for thederivative of the rate of glucose change.
 3. The method of claim 1,wherein (a) the set glucose readings exhibits one or morecharacteristics within a threshold amount of time before a secondcondition, (b) the set glucose readings exhibits one or morecharacteristics within a threshold amount of time after a secondcondition, (c) the set glucose readings exhibits one or morecharacteristics within a particular time range, or (d) the set glucosereadings exhibits one or more characteristics for a particular duration.4. The method of claim 1 further comprising applying a population-basedmodel of eating movement to the first set of acceleration readings, thepopulation-based model identifying a potential eating episode, whereinthe one or more glucose criteria comprise a criterion that the setglucose readings exhibit one or more characteristics within a thresholdamount of time of the potential eating episode.
 5. The method of claim 1further comprising: receiving a set of heart rate readings correspondingto a heart beat of the individual, wherein the set of heart ratereadings is captured during the first time period and determining thatthe set of heart rate readings satisfies one or more heart ratecriteria.
 6. The method of claim 5, wherein (a) a heart rate sensor iscoupled to the individual, and (b) the heart rate sensor captures theset of heart rate readings of the individual.
 7. The method of claim 1further comprising: receiving a first set of heart rate readingscorresponding to a heart beat of the individual, wherein (a) the firstset of heart rate readings is captured during the first time period, and(b) the machine learning model is further generated using at least aportion of the first set of heart rate readings as training data; andreceiving a second set of heart rate readings corresponding to the heartbeat of the individual, wherein (a) the second set of heart ratereadings is captured by the heart rate sensor during the second timeperiod, and (b) the prediction using the machine learning model is basedat least in part on the second set of acceleration readings and thesecond set of heart rate readings.
 8. The method of claim 7, wherein (a)a heart rate sensor is coupled to the individual, and (b) the heart ratesensor captures the first set of heart rate readings or the second setof heart rate readings.
 9. The method of claim 1 further comprisingproviding a notification of the second eating event.
 10. The method ofclaim 1, wherein the machine learning model is selected from the groupconsisting of an LSTM (long short term memory) model, a time distributedlayer recursive neural network classifier model, and a hidden Markovmodel.
 11. A system for using machine learning to detect eating activityof an individual, the system comprising one or more processors and oneor more memory storage areas, the system configured to: receive a set ofglucose readings, wherein (a) the set of glucose readings corresponds toglucose levels of the individual during a first time period, (b) the setof glucose readings is captured during the first time period by acontinuous glucose monitoring (CGM) device coupled to the individual,(c) the CGM device comprises a glucose sensor that collects CGM glucosereadings, and (d) the set of glucose readings is wirelessly transmittedto the system by the CGM device; determine that the set of glucosereadings satisfies one or more glucose criteria indicative of a firsteating episode; responsive to determining that the set of glucosereadings satisfies the one or more glucose criteria indicative of thefirst eating episode, determine whether a set of photo-plethysmograph(PPG) readings satisfies one or more PPG criteria indicative of thefirst eating episode, wherein (a) the set of PPG readings corresponds toblood flow during the first time period, (b) the set of PPG readings iscaptured during the first time period by a wearable device worn by theindividual, (c) the wearable device comprises both a PPG sensor thatcollects PPG readings and an accelerometer that collects accelerometerreadings, and (d) the set of PPG readings is wirelessly transmitted tothe system by the wearable device; responsive to determining that theset of PPG readings satisfies one or more PPG criteria indicative of thefirst eating episode, identify a first set of acceleration readings astraining Response dated data for training a machine learning modelconfigured to generate predictions of eating episodes, wherein (a) thefirst set of acceleration readings corresponds to motion of a hand ofthe individual, and (b) the first set of acceleration readings iscaptured during the first time period by the accelerometer of thewearable device; programmatically train the machine learning model,wherein the trained machine learning model (a) generates predictions ofeating episodes based at least in part on acceleration readings, and (b)is stored in one or more memory storage areas accessible by the system;a second set of acceleration readings, wherein (a) the second set ofacceleration readings corresponds to motion of the hand of theindividual, and (b) the second set of acceleration readings is capturedduring a second time period by the accelerometer of the wearable device;generate, using the trained machine learning model and the second set ofacceleration readings, a prediction that the second set of accelerationreadings indicates a second eating episode; and provide an indication ofthe prediction that the second set of acceleration readings indicates asecond eating episode to the wearable device, wherein the wearabledevice causes display of at least a portion of the indication.